Response Models

Response models define what data the API should return. They can also prevent sensitive fields from being exposed in the API response.

For example, a database user might contain a password, but you may not want to return the password to the client. A response model allows you to specify which fields should be included in the response.

Code

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class UserResponse(BaseModel):
    name: str
    email: str


@app.get("/users", response_model=UserResponse)
def get_user():
    return {
        "name": "Tarun",
        "email": "tarun@example.com",
        "password": "secret123"
    }

Understanding the Code

First, create a Pydantic response model:

class UserResponse(BaseModel):
    name: str
    email: str

The response model defines the fields that should be returned by the API.

In this example, the response model contains only:

name
email

The endpoint uses the model with response_model:

@app.get("/users", response_model=UserResponse)

Even though the function returns a password field:

return {
    "name": "Tarun",
    "email": "tarun@example.com",
    "password": "secret123"
}

the password is not included in the API response because it is not part of UserResponse.

Output

{
    "name": "Tarun",
    "email": "tarun@example.com"
}

The password field is excluded from the response.

Why Use Response Models?

Response models are useful for:

• Defining the structure of API responses
• Controlling which fields are returned
• Preventing sensitive fields from being exposed
• Keeping API responses consistent

For example, instead of returning all user information, you can define exactly what the client should receive:

class UserResponse(BaseModel):
    name: str
    email: str

Then use it in the endpoint:

@app.get("/users", response_model=UserResponse)
def get_user():
    ...

This ensures that the API response follows the defined response model.